https://github.com/simranshaikh20/customerchurnprediction

Customer churn prediction is to measure why customers are leaving a business. In this tutorial we will be looking at customer churn in telecom business. We will build a deep learning model to predict the churn and use precision,recall, f1-score to measure performance of our model.
https://github.com/simranshaikh20/customerchurnprediction

keras knn machine-learning pytorch

Last synced: about 1 month ago
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Customer churn prediction is to measure why customers are leaving a business. In this tutorial we will be looking at customer churn in telecom business. We will build a deep learning model to predict the churn and use precision,recall, f1-score to measure performance of our model.

• Host: GitHub
• URL: https://github.com/simranshaikh20/customerchurnprediction
• Owner: SimranShaikh20
• Created: 2024-08-25T04:51:43.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-12-17T11:22:17.000Z (over 1 year ago)
• Last Synced: 2025-10-27T08:48:11.249Z (8 months ago)
• Topics: keras, knn, machine-learning, pytorch
• Language: Jupyter Notebook
• Homepage:
• Size: 162 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# CustometChurnPrediction

Customer churn prediction is to measure why customers are leaving a business. In this tutorial we will be looking at customer churn in telecom business. We will build a deep learning model to predict the churn and use precision,recall, f1-score to measure performance of our model.

## Project Overview

This project focuses on predicting customer churn in the telecom industry using deep learning techniques. Customer churn, which measures why customers are leaving a business, is a critical metric for companies to understand and address.

## Objectives

- Analyze customer churn patterns in the telecom business

- Build a deep learning model to predict customer churn

- Evaluate the model's performance using precision, recall, and f1-score metrics

## Implementation

Our approach includes:

- Collecting and preprocessing telecom customer data

- Developing a deep learning model for churn prediction

- Training and validating the model using appropriate datasets

- Evaluating the model's performance using precision, recall, and f1-score

## Technologies Used

- Python

- Deep Learning libraries (e.g., TensorFlow or PyTorch)

- Data analysis and visualization tools

## Key Features

- Data preprocessing and feature engineering tailored for telecom customer data

- Implementation of a deep learning model for churn prediction

- Comprehensive evaluation using multiple performance metrics

## Why This Matters

Understanding and predicting customer churn is crucial for businesses, especially in the competitive telecom industry. By accurately identifying potential churners, companies can:

- Implement targeted retention strategies

- Improve customer satisfaction and loyalty

- Optimize resources by focusing on at-risk customers

## Results

The project demonstrates the effective application of deep learning in predicting customer churn. Detailed results, including model performance metrics, are available in the project files.